Abstract
Retaining users and customers is one of the most important challenges for the service industry from mobile communications to online gaming. As the users of these services form dynamic networks that grow in size, predicting ‘churners’ becomes harder and harder. In this work, we explore the use of anomaly detection for churn prediction. To this end, we evaluate bio-inspired and deterministic online clustering algorithms on both cell phone and online gaming data sets. We discuss the results of each technique from the perspective of: feature identification, sensitivity analysis of the parameters as well as their capacity to detect churn.
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Acknowledgement
This research is supported by the Mitacs Accelerate Internship grant, and is conducted as part of the Dalhousie NIMS Lab at: https://projects.cs.dal.ca/projectx.
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Tatar, S.B., McIntyre, A., Zincir-Heywood, N., Heywood, M. (2015). Benchmarking Stream Clustering for Churn Detection in Dynamic Networks. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_24
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DOI: https://doi.org/10.1007/978-3-319-24282-8_24
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